Image saliency detection method based on multifeature maps fusion

In this research, we introduce an innovative saliency detection algorithm, comprising three essential steps. Firstly, leveraging fully convolutional networks with aggregation interaction modules, we generate an initial saliency map. Secondly, we extract hand-craft and deep features to express the image, then use manifold ranking method to construct saliency maps. Ultimately, by integrating the outcomes from preceding stages, we generate the final saliency map. Experimental findings demonstrate that our method surpasses twelve cutting-edge saliency detection techniques in terms of precision, recall, F-measure, and MAE value metrics."

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Publication Details

Published
2024-10-09
DOI
https://doi.org/10.1117/12.3045835
Primary Topic
Visual Attention and Saliency Detection
Type
article
Field-Weighted Citation Impact
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article

Image saliency detection method based on multifeature maps fusion

Xiaoli Li, Yunpeng Liu, huaici zhao
Visual Attention and Saliency Detection
article

Image saliency detection method based on multifeature maps fusion

Xiaoli Li, Yunpeng Liu, huaici zhao
article en

Abstract

In this research, we introduce an innovative saliency detection algorithm, comprising three essential steps. Firstly, leveraging fully convolutional networks with aggregation interaction modules, we generate an initial saliency map. Secondly, we extract hand-craft and deep features to express the image, then use manifold ranking method to construct saliency maps. Ultimately, by integrating the outcomes from preceding stages, we generate the final saliency map. Experimental findings demonstrate that our method surpasses twelve cutting-edge saliency detection techniques in terms of precision, recall, F-measure, and MAE value metrics."

Shenyang Institute of Automation (CN), Guangdong Institute of Intelligent Manufacturing (CN), University of Chinese Academy of Sciences (CN)
Openalex Percentile: Top 83%
Visual Attention and Saliency Detection
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